{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "np.random.seed(sum(map(ord, \"categorical\")))\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "iris = sns.load_dataset(\"iris\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 画树莓图 ####\n",
    "* 遇到时间的时候我们都会发现时间并不连续【例如星期】如果画图离散度不太好如何通过抖动直接可以看到找到离散区间\n",
    "* stripplot进行图示展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x86c84e0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gjx4NAJgwYQKOHDnSraSfl5fnihCdVlrZLuizGPRuGy8hBZfLIKsWzk+TzrXr\nLTh7+vqRiVYLjy/+dRzpE4I7eZT7cknS12g67na3trbij3/8I5YtW4aNGzfalt9pNBq0tHTvlKm0\ntDRXhOg0w+kKAHV2fXKV2m3jJd7liIO+EampUAYKT4EinSu9WAfA/pxcfYv75iag88Gyy673Kisr\nsXjxYsyePRuzZs2yu7TU6XQIlPybT7h210pVDImb8IvpZ9eW+/lB4U8nu4mhdHCIispHupvcXJL0\n6+rq8Pjjj+NPf/oT5s6dCwAYPHgwcnNzAQCHDx9Genq6K56611gd5Hcpn6ZDPEvSU78Hp/zlHpNM\nhsQnHgNHc/qiRPXrg8Ag+1PH0sZK99wMl7wL3n77bTQ3N+Ott97CokWLsGjRIixbtgzZ2dlYsGAB\nTCaT3Zy/FFXUtgr6qhtpySZxD1V79oA3/VIB1mpF5dffsA1IwmRyGe6+fyj8NErI5BzikoIxSsJJ\n3yXXKKtXr7ar1XPNtm3bXPF0TJRVNgv6DEYa6RP3UJ+Ta9fWFV+CWa+nw9FFsJit+HRbHkymjh34\nl4sbsP/rc5h2nzT3kdD1nkhRDnbfUq014i5so/wb++iekyjF52tsCf+avJwyRtE4j5K+SDIHJ4fR\nwejEnVloR64otVXClYYmCV/VU9IX6e47EgR9k9Jiez8QQrrJbDSyDkGSfByUYZBwFQZK+qLxwv94\nGukTt3ZzUXjSLdExfQR9Uj43g5K+SN+fuio4Qu3Aj1fYBENIN6gjI1mHIEnFRXWCPotFuvdHKOmL\npPEVjupVDjZxEMKCzNfXQSd93MWQKzzrdfOs36YXjRosPAR9ULw0a3G4C4vVgh/LT6Go9iLrUCTP\n6mD+3twq3FtCusY72okpYdLdS8zY598JE9O+E5fxu6xhDKKRvnpdI/7vNy+izdxRyC42MBqv3v0X\nxlFJl0yhECR+ZUAAo2ikLTxaWDKG5vS90M9FtYI+2pwl3pYft9kSPgBcaa7AwUtHGUYkbVaz8GQn\nSxvtGBdHONKn1TteKCVBOJVDh3iLV9Z0VdBXWHuBQSQewipcqeNZkxS9p7nJsw6foaQvkrZZWE+f\no4+VaH19hcviYgOjGURCiL2BDu7fBfTxYxBJz6CkL9LIFAdvBH8Vg0g8Q1r0UEHf8KjBDCLxXMYm\nOs5TjJZm4UhfLpfuVT0lfZECNcIEPzCOVu+IpTMJ55vr9Q0MIvFcCj8qtiZGe5uwjlF7u/CeiVRQ\n0hcpPipQUGBtYHxfNsF4ABknfCvSZFnPkvs5WLtPuqT2d7TTXrrvTkr6IkWGaDBlVByu5f2IYDVm\nOqjHQ7pnatJ4qJXX50njg2LKtouCAAAPE0lEQVQwPJKmd3qSRa9nHYIkNdQKC9W16YWjf6mgdfoi\n1Te1Yf+Jy7bv++oGPfaeuILZE5KYxiVVUQHheHXGX3D0ch7USl/cGT8KChntcBaN43BznRA5Te+I\n4jDBS3egTyN9sU4W1Qpq7+yRcI1tdxCi7otZKZm4K2kcfBU+rMORNEdlGGQqKggoRmi48GxhlY90\nBySU9EW6UiOssa1t9az1vES6HB2iYjVKd0qCJY2DVXlSrsdD0zsiDYgJEvRFOjhNi3RfQU0RdhZ8\nDY1Kjd+MmItQDa2GEou3CHeH81RaWZSyS8JVZG066X6BUtIXaXBiiKAvMUpYo4N0T17FaWz8fvMN\n7Xy8P2cTfJW04qTnSHdtOUtqByN9qr3jhb49Xiroyyuq6f1APMS/8nfbtc1WMz47u4dRNJ5J7kOb\nB8UIDRcWqnP0RSAVlPRFqm0UbibSttCcvlgyB7XelXK6EBXNwSHo7TU0KBGjWStc6moxS3eqjJK+\nSFeqhTdyrR5Wd7s3LRr+gF1bJVfivoFTGUXjmXxCQ1mHIEmaAOEUo8pHugMSSvoiJUY7ODdTwvU4\nWPNR2F8uq+V+sEp5MbQbMjZQWQsxHBXPNZmkW0adkr5YDi6fKeeL9/X5g3ZtrbEZP1XkM4rGM8n9\npFsZkqXaKuGJY2aa3vE+MrnwpZNy5T3WLjSUCvpKtcIa+6R7lH3sr0Q5hRyqPsKrU9K15EHhgrMy\nEgdId6qMkr5Iw5KE/+lBATSS6klGM90YF8tisj8qkTdbYNbTyVliqHwUmPdoGvw0HTuaEwaE4r4F\nIxhHJZ5070YwVl7r4JLPIt1LPtbSo4fhy6K9dn3j4zMYRSN91nbhFyanpI+7WCmpUUhJjYLVYnV4\nlS8l0o6eIY2vsI4J72Cen3RPH19hfRM5FVwTz8FxieYW4UCF/DpST/gAJX3R2o3CQxRkdEauaJUt\nwoPmy5srGETiuRRqmn4klPRFi4kQ7tIL60u1d8S6PWqIXVspVyI1fCCjaDwTb5buMkN3YfWAKVya\n5BMpyF9Y+revgz7SPaNjRuC3aQ9i78XvoVGpMXfITAT50WqTnmR1UISNdM8Xn/yMUyfKAQAKhQxP\nr7oLAX2kWRfKpSP9U6dOYdGiRQCAsrIyPPTQQ3j44Yexdu1aWB3MOUrJuTLhRpezDvpI91itVlxt\nrkZlaw2utlSjRlfPOiSPY2ppZh2CJDU36m0JH+hYo7/5vw+xC8hJLkv67777LlavXg2DoWMVwfr1\n67Fs2TJ8/PHH4Hke+/fvd9VT94oKB6t3dA4OUCbdc7DkKL6+cBBGiwlN7c3YcmIbKluoVoxoDu4v\nqaOiGAQifd/tuyDoa5fwcYkuS/pxcXHIzs62tQsKCjB69GgAwIQJE3D06FFXPXWvCAtyUI9Dwgcr\nsHahvsSuzYPHxfpSNsF4AEe7by0G2vcgRtJtYaxD6FEum9OfPn06ysuvXxLxPG/b1abRaNDSIixY\n5kheXp5L4nNWY51wpO+n4t02Xnfnq7N/K3LgYKzSI6+eXk8xLDevyVcocLKwULCzlHSt+kq7oE8m\nc9/c1JVeu5F7Y+lcnU6HwMDuHTiSlpbmqpCcEhrdjC9y7evFjB+ZgLS0Ibd4BOnMSH4kVPl+2Ff8\nAzRKPywYeh/GxY9iHZZk5fKA3aJisxkjBg+hZZsi5HPl+BH29+vkCrnb5iag8y+kXkv6gwcPRm5u\nLjIyMnD48GGMGTOmt57aJeKjAjFnYhK+OFwMngf6R/fBnIkDWIclWRzH4eFhc/DwsDmsQ/EI6rhY\nNJ8psLVVISGQ+9LqMjFShkaB407abb5MGijdKZ9em4ResWIFsrOzsWDBAphMJkyfPr23ntplpo6O\nQ3JsECJD1Lj7jnj0oSWbTvmqaD+W//s/8fy+V3CyspB1OJLW/8nH4RPekZgUAQEYsPQpcA4OqiFd\nUyrl6Btif4U0MDWSUTTO43g3rh2Ql5fntpdQbQYzHlz9ld1u9yVzUjFrfBK7oCTs2JWf8D9H37W1\nlTIFsu99CcF+wgPoSffwFgvaq6rhExYKmUq6x/uxVl3RjC2vfmfX1/+2UDzyu7GMIupaZ7mTvvpF\n2r7vvKC8ySd7z7MJxgOcrLIf2ZusZhRU0+vpDE4uh1+/aEr4TvJTKwUrYNUa6V7VU9IXqc0grL1j\nsbjtRZPbi+/TT9gXJOwj3ddy4SLKP/0c2pOnWIciaYFBfsiY0N/W9lMrMe4u6d6/ozIMImVNTsbu\nI/ZryyelUZISKzNpHIrqLyHnSh5UMiUeGHIP4ijpi1Zz4CAuvP43WztmbhbiFy1kGJG0TbtvCIan\nx0LboEfCgFD4+Eo3dUo3csY0fgpwnP2pieFUcE00pVyJZWOfwJNpD0EpU0CloCkJZ5Tv/MyuXbFr\nN2IfnA+ZUlgSnHTNarGirqYV2gY9QsI08PEVFlyUCpreEWn3D5cEx+R+k1PGJhgPolGpKeH3AHOr\nzq5tNZsdnutMumfH1jzs3JqH/V+dxduvfofSi3WsQxKNkr5IIX2Em1zUEr7kI57GQYKn3bii1Ne2\n4lx+la1ttfA4dvgSw4icQ0lfpCnpsQi9obQqxwFL7h/KMCJCrpOrNXZtTi6nkX5PkvBLSUNTkTiO\nw7vPT8We3DLUNeoxa1x/BDsY/RPCQr/7Z6P4zc22dvSsmbR0U6SQMH8MHBKBooJqAIBMziFjYv8u\nHuW+KOk7QSGXYeYdiazDIEQgclom1LExaMo/A03/RASnu+cmR6mY+5t0nD1VicYGPVJSIxEWKd0b\nuZT0idvQm9rwU0U+1Eo1RkQOtivSR369wEEpCByUwjoMjyCXy5A60jOWEFPSJ26hXt+IVfs2orGt\nCQAwJPw2/GXSM5BxlPgJ6Un0iSJuYc/F72wJHwAKas7jTHURw4gI8UyU9IlbMJiNgr52M530REhP\no6RP3MLkxDuglF/fLRquCcGIyMEMIyLEM9GcPnELCX1jsD5zBb4rPQaNSo27+t9JO3MJcQFK+sRt\nxAX1w6IRD7AOgxCPRtM7hBDiRSjpE0KIF6GkTwghXoSSPiGEeBG6kUuIh2oqKEDTqXxokvojePQo\ncFRamYCSPiEeqeqbb1G8eYutHT17FhIff5RdQMRt0PQOIR7o6he77NqV//4GVqNw1zPxPpT0CfFA\n3E0VSjmOo5OzCABK+oR4pJgHsuySfPSc++hQdAKA5vQJ8UjhUyZBHR+HptMdN3KDhtFRnqQDJX1C\nPJR/Un/4J0n3WD/iGjS9QwghXoSSPiGEeBFK+oQQ4kUo6RNCiBehpE8IIV6Ekj4hhHgRSvqEEOJF\nKOkTQogXcfvNWXl5eaxDIIQQj8HxPM+zDoIQQkjvoOkdQgjxIpT0CSHEi1DSJ4QQL0JJnxBCvAgl\nfUII8SKU9AkhxIu4/Tp9ljZs2ICCggLU1taivb0dsbGxuHDhAsaOHYvXXnuNdXgeoby8HPfddx+G\nDBli68vIyMDSpUtt7eXLl2Pjxo1QqVQsQpSEd955B0ePHoVMJgPHcVi+fDlSU1Md/uw///lPZGVl\nQUnHJzr0a15LSeJJl3bu3Mlv2rSJ53meP3bsGL9s2TLGEXmOK1eu8PPmzWMdhqRduHCBX7BgAW+1\nWnme5/nCwkJ+1qxZt/z5yZMn8+3t7b0VnqT82tdSimh6R4SysjL89re/RVZWFrKzswEAixYtQnFx\nMQDgH//4B7Kzs1FeXo5Zs2Zh0aJFePfdd1mGLCm5ubmYN28eHn74YXz++eeYMmUKDAYD67DcVnBw\nMCoqKrBjxw5UV1dj0KBB2LFjB44fP47Fixdj8eLFmD9/PkpKSrB9+3bU1tZi+fLlrMN2S7d6LW/1\n+V6wYAGeeeYZZGVlYe3atYyj7x6a3hHBYDDgrbfegsViwaRJk/D000/f8mdra2uxc+dOmproxMWL\nF7Fo0SJbe968eTAYDNi+fTsA4I033mAVmiQEBwdj8+bN2LZtG9588034+vpi+fLlqKurw6ZNmxAR\nEYG3334b33zzDZ566ils3ryZpidv4Vav5a2Ulpbi73//O/z8/JCZmYna2lqEhYX1YsS/HiV9EZKT\nk21JXKEQvoT8DZUtYmJiKOF3YcCAAdi6dautnZubi8TERIYRSUtZWRn8/f2xfv16AEB+fj6WLFmC\nP//5z3j55ZehVqtRXV2NkSNHMo7U/d3qtQwNDbX9zI2f77i4OPj7+wMAwsLCJHFFStM7InAcJ+hT\nqVSora0FABQWFtr6ZTJ6icWg1637ioqK8MILL9gSTmJiIgICArBu3TqsW7cOGzZsQHh4uC1ZcRwH\nq9XKMmS3davXMigoyOHn21EucHc00u8hixcvxosvvoioqCiEh4ezDod4kWnTpqG4uBjz5s2DWq0G\nz/P485//jBMnTmD+/PkIDAxEaGgoampqAADp6elYsmQJPvzwQ0kmLVe61WupVCo95vNNVTYJIcSL\n0DU0IYR4EUr6hBDiRSjpE0KIF6GkTwghXoSSPiGEeBFK+oR0w8qVK/Hpp5+yDoMQp1HSJ4QQL0Lr\n9AlxgOd5bNiwAYcOHUJ4eDgsFgvmzp2LsrIy5OTkoKmpCeHh4Xjttddw8OBBHDt2DK+++ioAIDs7\nGz4+PliyZAnj34IQIRrpE+LAnj17UFhYiN27d+P111/H5cuXYbFYcOnSJXzyySfYs2cPoqKisGvX\nLtxzzz3IyclBa2srAGD37t2YPXs249+AEMeoDAMhDhw/fhzTpk2DUqlEcHAwJkyYALlcjhUrVmD7\n9u0oKSnByZMnERcXB41Gg4kTJ2Lv3r2IjY1FbGwsIiIiWP8KhDhEI31CHOA4zq6aokKhgFarxRNP\nPAGr1Yrp06cjMzPT9jMPPPAAdu/ejS+//BJZWVmswiakS5T0CXFg7Nix+Prrr2E0GtHU1ITvv/8e\nHMdh9OjReOihh5CQkIBDhw7BYrEA6ChiVlVVhdzcXGRmZjKOnpBbo+kdQhzIzMxEfn4+7r33XoSG\nhiIpKQnt7e04d+4cZs2aBQBITU1FeXm57TFTp06FVqul8xOIW6PVO4Q4ied5mEwmPPbYY1i1apXd\nIe+EuBua3iHESbW1tbjzzjsxfPhwSvjE7dFInxBCvAiN9AkhxItQ0ieEEC9CSZ8QQrwIJX1CCPEi\nlPQJIcSL/H+wSzvj556NQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x858ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='day',y='total_bill',data=tips) #从这张图我们可以看出来离散度很不好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "重叠是很常见的现象，但是重叠影响我观察数据的量了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xcefb198>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nq/ZitcioVAqW3TyJ4aO69ozU17SxZV0xDbXtVJR29e4VCon/e+C8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc8388d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x='day',y='total_bill',data=tips,jitter=True)   #jitter抖动偏移量进行,注意stripplot其实只适合小数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x89212b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o9Xrsdjs2m426ujqxcboguJDVaGTfw49hanWsCm/O2Uj6X57CKySYxp9W0Vlw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\naplSqZBLiQn1ZId1XWUkEglvPD6LQ+llKBQypo4JRKWUEx7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x86c8cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips)#它使用避免重叠点的算法将分类轴上的每个散点图点定位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function swarmplot in module seaborn.categorical:\n",
      "\n",
      "swarmplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, ax=None, **kwargs)\n",
      "    Draw a categorical scatterplot with non-overlapping points.\n",
      "    \n",
      "    This function is similar to :func:`stripplot`, but the points are adjusted\n",
      "    (only along the categorical axis) so that they don't overlap. This gives a\n",
      "    better representation of the distribution of values, although it does not\n",
      "    scale as well to large numbers of observations (both in terms of the\n",
      "    ability to show all the points and in terms of the computation needed\n",
      "    to arrange them).\n",
      "    \n",
      "    This style of plot is often called a \"beeswarm\".\n",
      "    \n",
      "    A swarm plot can be drawn on its own, but it is also a good complement\n",
      "    to a box or violin plot in cases where you want to show all observations\n",
      "    along with some representation of the underlying distribution.\n",
      "    \n",
      "    Note that arranging the points properly requires an accurate transformation\n",
      "    between data and point coordinates. This means that non-default axis limits\n",
      "    should be set *before* drawing the swarm plot.\n",
      "    \n",
      "    Input data can be passed in a variety of formats, including:\n",
      "    \n",
      "    - Vectors of data represented as lists, numpy arrays, or pandas Series\n",
      "      objects passed directly to the ``x``, ``y``, and/or ``hue`` parameters.\n",
      "    - A \"long-form\" DataFrame, in which case the ``x``, ``y``, and ``hue``\n",
      "      variables will determine how the data are plotted.\n",
      "    - A \"wide-form\" DataFrame, such that each numeric column will be plotted.\n",
      "    - Anything accepted by ``plt.boxplot`` (e.g. a 2d array or list of vectors)\n",
      "    \n",
      "    In most cases, it is possible to use numpy or Python objects, but pandas\n",
      "    objects are preferable because the associated names will be used to\n",
      "    annotate the axes. Additionally, you can use Categorical types for the\n",
      "    grouping variables to control the order of plot elements.    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y, hue : names of variables in ``data`` or vector data, optional\n",
      "        Inputs for plotting long-form data. See examples for interpretation.        \n",
      "    data : DataFrame, array, or list of arrays, optional\n",
      "        Dataset for plotting. If ``x`` and ``y`` are absent, this is\n",
      "        interpreted as wide-form. Otherwise it is expected to be long-form.    \n",
      "    order, hue_order : lists of strings, optional\n",
      "        Order to plot the categorical levels in, otherwise the levels are\n",
      "        inferred from the data objects.        \n",
      "    split : bool, optional\n",
      "        When using ``hue`` nesting, setting this to ``True`` will separate\n",
      "        the strips for different hue levels along the categorical axis.\n",
      "        Otherwise, the points for each level will be plotted in one swarm.\n",
      "    orient : \"v\" | \"h\", optional\n",
      "        Orientation of the plot (vertical or horizontal). This is usually\n",
      "        inferred from the dtype of the input variables, but can be used to\n",
      "        specify when the \"categorical\" variable is a numeric or when plotting\n",
      "        wide-form data.    \n",
      "    color : matplotlib color, optional\n",
      "        Color for all of the elements, or seed for :func:`light_palette` when\n",
      "        using hue nesting.    \n",
      "    palette : seaborn color palette or dict, optional\n",
      "        Colors to use for the different levels of the ``hue`` variable. Should\n",
      "        be something that can be interpreted by :func:`color_palette`, or a\n",
      "        dictionary mapping hue levels to matplotlib colors.    \n",
      "    size : float, optional\n",
      "        Diameter of the markers, in points. (Although ``plt.scatter`` is used\n",
      "        to draw the points, the ``size`` argument here takes a \"normal\"\n",
      "        markersize and not size^2 like ``plt.scatter``.\n",
      "    edgecolor : matplotlib color, \"gray\" is special-cased, optional\n",
      "        Color of the lines around each point. If you pass ``\"gray\"``, the\n",
      "        brightness is determined by the color palette used for the body\n",
      "        of the points.\n",
      "    linewidth : float, optional\n",
      "        Width of the gray lines that frame the plot elements.    \n",
      "    ax : matplotlib Axes, optional\n",
      "        Axes object to draw the plot onto, otherwise uses the current Axes.    \n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    ax : matplotlib Axes\n",
      "        Returns the Axes object with the boxplot drawn onto it.    \n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    boxplot : A traditional box-and-whisker plot with a similar API.    \n",
      "    violinplot : A combination of boxplot and kernel density estimation.    \n",
      "    stripplot : A scatterplot where one variable is categorical. Can be used\n",
      "                in conjunction with other plots to show each observation.    \n",
      "    factorplot : Combine categorical plots and a class:`FacetGrid`.    \n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    \n",
      "    Draw a single horizontal swarm plot:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> import seaborn as sns\n",
      "        >>> sns.set_style(\"whitegrid\")\n",
      "        >>> tips = sns.load_dataset(\"tips\")\n",
      "        >>> ax = sns.swarmplot(x=tips[\"total_bill\"])\n",
      "    \n",
      "    Group the swarms by a categorical variable:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips)\n",
      "    \n",
      "    Draw horizontal swarms:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"total_bill\", y=\"day\", data=tips)\n",
      "    \n",
      "    Color the points using a second categorical variable:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"day\", y=\"total_bill\", hue=\"sex\", data=tips)\n",
      "    \n",
      "    Split each level of the ``hue`` variable along the categorical axis:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
      "        ...                    data=tips, palette=\"Set2\", dodge=True)\n",
      "    \n",
      "    Control swarm order by passing an explicit order:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"time\", y=\"tip\", data=tips,\n",
      "        ...                    order=[\"Dinner\", \"Lunch\"])\n",
      "    \n",
      "    Plot using larger points:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.swarmplot(x=\"time\", y=\"tip\", data=tips, size=6)\n",
      "    \n",
      "    Draw swarms of observations on top of a box plot:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.boxplot(x=\"tip\", y=\"day\", data=tips, whis=np.inf)\n",
      "        >>> ax = sns.swarmplot(x=\"tip\", y=\"day\", data=tips, color=\".2\")\n",
      "    \n",
      "    Draw swarms of observations on top of a violin plot:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> ax = sns.violinplot(x=\"day\", y=\"total_bill\", data=tips, inner=None)\n",
      "        >>> ax = sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips,\n",
      "        ...                    color=\"white\", edgecolor=\"gray\")\n",
      "    \n",
      "    Use :func:`factorplot` to combine a :func:`swarmplot` and a\n",
      "    :class:`FacetGrid`. This allows grouping within additional categorical\n",
      "    variables. Using :func:`factorplot` is safer than using :class:`FacetGrid`\n",
      "    directly, as it ensures synchronization of variable order across facets:\n",
      "    \n",
      "    .. plot::\n",
      "        :context: close-figs\n",
      "    \n",
      "        >>> g = sns.factorplot(x=\"sex\", y=\"total_bill\",\n",
      "        ...                    hue=\"smoker\", col=\"time\",\n",
      "        ...                    data=tips, kind=\"swarm\",\n",
      "        ...                    size=4, aspect=.7);\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sns.swarmplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd44b940>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atzVsgDNKlZol7x7gxQ+SeOLf+2hoMhYR23BuO0t2/JMPj33Jk1v/xuHC1PYe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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89050f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips,hue=\"sex\") #按照哪一类进行分组【观察两个特征之间的关系】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd577e10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nph2D1a21+Dh4MjIw3uyaWUV1Jn0IOp2eTQfzzYIHgJO1+R9uV0/ZXSltqjBL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\nQxAEQbCZCB6CIAiCzf4/8On1iB2XH9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd4ba208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x='total_bill',y='time',data=tips,hue=\"sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 盒图  ###\n",
    "* IQR即统计学概念四分位距，第一/四分位与第三/四分位之间的距离\n",
    "* N = 1.5IQR 如果一个值>Q3+N或　<　Ｑ1-N,则为离群点\n",
    "* 其实离群点的含义就是说假如数据明显不符合业务或者明显错误就可以将这些数据筛选出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd5e8b70>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEFCAYAAAAPCDf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5c7470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"day\", y=\"total_bill\", data=tips,hue=\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xda092e8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HpqYmMI4p83G9wAw82tra4HK5YmxZ70et3Aw2tKPCCRx0fYw4efJkSvet6gqR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\nEwRBpBD/P41MJiRr0RLiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7923c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"day\", y=\"total_bill\", data=tips,hue=\"time\") \n",
    "#小提琴图我们可以看数据重复程度，但是问题来了，目前重合部分的我们无法表达如何操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xeadfac8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0tDRoNMIvR5d96APAo48+ioiICFhLf4bTXCF1ObKWmJiItLQ0qJRK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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5c7a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"day\", y=\"total_bill\", data=tips,hue=\"time\",split=True) \n",
    "#我们可以通过split解决数据堆叠重合问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 嵌套图使用场景####\n",
    "* 查看整个数据重叠区域具体点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xec7de80>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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goDEZiUzSHjHs9bJKq6MxHGL//v03rei3tbUBcPvtt7NqVR0ASUXh4vlehn0h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J9h2lWPkaB3J9KkpKQwViIiSUhD32Kx8NRTT4XyRwqTsFqtZGdnU13zGXpbsBdPTk5OmKsyt96h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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xec66d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"day\", y=\"total_bill\", data=tips, inner=None) #inner其实去掉中间堆叠Q3和Q4的划线\n",
    "sns.swarmplot(x=\"day\", y=\"total_bill\", data=tips, color=\"w\", alpha=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显示值的集中趋势可以用条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CDk/U40dLwWf3LqDMzEw88MADyM3NhV6vR3h4OObOnWt3oxEREdi5cycAoKysDKGhobZ1\nffv2RWVlJc6ePQuLxYJ9+/YhJCSkNftCRNSm6HQ6REVF2X43bU/sngGo1WqMGDECQUFBeOyxx3Dq\n1CnbN/mWREdHY/fu3YiPj4ckScjKysKKFSug1WoRFRWFlJQUTJ48GQDwzDPPNAsIIqL2oL6+HoWF\nhQCAoqIiTJkyBZ6eni6uqvXsHsm3bt2KJUuW4PLly8jNzUV8fDxmzZqFMWPGtPg+pVKJjIyMZsuC\ng4Ntr0eNGoVRo0b9zrKJiFzPbDZDkiQAVy5zm83mdhUAdi8BLV26FOvWrYOXlxe6deuGr7/+Gl99\n9ZUzaiMiIhnZDQClUtnsbp7u3btDqeQAYiKi9s7uJaD7778fq1evhsViwaFDh7B27Vr07t3bGbUR\nEZGM7H6VT0tLw6+//opOnTrhvffeg0ajadVdQERE1LbZPQPIz8/HhAkTkJKS4ox6iIjISeyeAZw+\nfRqxsbGYPHkyCgsLUV9f74y6iIhIZnYDIDU1FTt27MC0adNQVlaG559/HrNmzXJGbUREJKNW3c4j\nSRIaGxvR2NgIhUIBd3d3uesiIiKZ2f0NIDMzE9999x3CwsIwevRozJ49G506dXJGbUREJCO7AdCr\nVy98/fXXnK6ZiKiDuWUA6PV6xMXFoa6uDmvXrr1h/fTp02UtjIiI5HXL3wCuzm9BREQd0y3PAOLj\n4wEA3t7eePbZZ9GtWzenFUVERPLjOAAiIkFxHAARkaDsP9kFHAdARO3f1vETHd5nfVNTs3bxG0nw\nVKkc1v/IVSsc1tfNcBwAEZGg7AaAn58fxwEQEXVAdn8DKCoq4sGfiKgDsnsGEBISgs8//xz9+/eH\nh4eHbfmgQYNkLYyIiORlNwDOnz+P0tJSlJaW2pYpFAqsWrVK1sKIiEhedgMgJyfHGXUQEZGT2Q2A\nxMREKBSKG5bzDICIqH2zGwBJSUm21xaLBdu3b0eXLl1kLYqIiORnNwAGDx7crP3II48gNjYWb775\npmxFtSc6nQ6bNm3CmDFjkJyc7OpyiIhazW4AnDx50vZakiQcPnwY58+fl7Wo9qK+vh6FhYUArtwu\nO2XKFHh6erq4KiKi1rEbAK+88goUCgUkSYJSqUTXrl0xZ84cZ9TW5pnNZtu02VarFWazmQFARO2G\n3YFgn376KV5++WV888030Gq1OHr0qDPqIiIimdkNgHnz5iE0NBTffvstPDw8sHHjRnz22WfOqI2I\niGRkNwCsVisee+wxlJSUYMSIEejZsyearpsBj4iI2h+7AeDp6Ym//e1vKC0txfDhw7Fq1Sp4eXk5\nozYiIpKR3QD46KOPcOnSJeh0Ovj4+ODXX3/Fxx9/7IzaiIjaNNU1g2QV17XbA7t3Ad19992YPn26\nrT1z5kxZCyIiai/USiXCvTQoMxnR30sDtdLud+o2RbZqrVYr0tLSEBcXh8TERNTU1Nz0byZPnox1\n69bJVQYRkayifP2Qco8WUb7tb9p82QKguLgYZrMZer0eKSkpyM7OvuFv/vKXv6Curk6uEoiIqAWy\nBYDBYMDQoUMBAOHh4Th48GCz9d988w0UCgUef/xxuUogIqIWtOqh8L+H0WiERqOxtVUqFSwWC9zc\n3FBZWYnNmzdDp9Phiy++aHWfBoNBjlJ/N5PJ1Ky9b98+3iFFRA4j9zFPtgDQaDTNDpBWqxVublc2\nt3HjRvz666949dVXceLECbi7u+Oee+6xezYQGRkpV7m/y/WXr/r37w8fHx8XVUPtmr7c1RVQG+SI\nY15LISJbAERERKCkpAQjR45EWVkZQkNDbetmzZple71o0SLcddddvBRERORksgVAdHQ0du/ejfj4\neEiShKysLKxYsQJarRZRUVFybZaIiFpJtgBQKpXIyMhotiw4OPiGv7v2gTNEROQ87WvUAhEROYxs\nZwBt0bhZaxzan9VyuVl76gfroXTzcFj/axe87LC+iIiuxzMAIiJBMQCIiATFACAiEhQDgIhIUAwA\nIiJBMQCIiATFACAiEhQDgIhIUAwAIiJBMQCIiATFACAiEhQDgIhIUAwAIiJBMQCIiATFACAiEhQD\ngIhIUAwAIiJBMQCIiATFALgTCtW1jevaRERtGwPgDihV7vD0DwMAePr3hlLl7uKKiIhaT6iHwsuh\ni3YIumiHuLoMIqLbxjMAIiJBMQCIiATFACAiEhQDgIhIUAwAIiJBMQCIiATFABCQTqdDVFQUdDqd\nq0shIhdiAAimvr4ehYWFAICioiLU19e7uCIichUGgGDMZjMkSQIAWK1WmM1mF1dERK7CACAiEhQD\ngIhIULLNBWS1WpGeno6Kigqo1WpkZmaiV69etvUrV67Eli1bAADDhg3D9OnT5SqFiIhuQrYzgOLi\nYpjNZuj1eqSkpCA7O9u27tixYygsLERubi70ej3+8Y9/oLy8XK5SiIjoJmQ7AzAYDBg6dCgAIDw8\nHAcPHrSt69GjB5YtWwaV6sr8+RaLBZ06dWpVnyKRY39NJlOz9r59++Dl5eXw7RDRnZP7mCdbABiN\nRmg0GltbpVLBYrHAzc0N7u7u8PPzgyRJWLBgAR588EEEBgba7TMyMvLOitK3r7OMO97fm6irq2vW\n7t+/P3x8fBy+HbpN7eyzSc7hiGNASyEi2yUgjUbT7Num1WqFm9v/86ahoQFvv/02TCYT5s6dK1cZ\nRER0C7IFQEREBHbu3AkAKCsrQ2hoqG2dJEl444038MADDyAjI8N2KYiIiJxHtktA0dHR2L17N+Lj\n4yFJErKysrBixQpotVpYrVb8+OOPMJvN2LVrFwDgrbfewoABA+Qqh4iIriNbACiVSmRkZDRbFhwc\nbHt94MABuTZNHYxOp8OmTZswZswYJCcnu7ocog6DA8GoTePcRUTyYQBQm8a5i4jkwwAgIhKUbL8B\n0J2bsOJNh/fZ1GBp1p6+7j2oOjnuY7By4mcO64uI5MUzACIiQTEAiIgExQAgIhIUA4CISFD8EZgc\nauv4iQ7tr76pqVm7+I0keDp46pCRq1Y4tD+i9oJnAEREgmIAEBEJigFARCQoBgARkaAYAEREgmIA\nEBEJigFARCQoBgC1aSqFwvZacV2biO4MA0AwCuU1B1DFde02SK1UItxLAwDo76WBWsmPLJGjcCSw\nYJTuKmhC/WCsPAvN/X5Qujt2VK0conz9EOXr5+oyiDocBoCAug4OQNfBAa4ug4hcjOfTRESCYgAQ\nEQmKAUBEJCgGABGRoBgARESCYgAQEQmKAUBEJCgGABGRoBgARESCYgAQEQmKAUBEJCgGABGRoBgA\nRESCki0ArFYr0tLSEBcXh8TERNTU1DRbn5eXh5iYGLz00ksoKSmRqwwiIroF2aaDLi4uhtlshl6v\nR1lZGbKzs7FkyRIAQG1tLXJycrBhwwY0NDRg3LhxePTRR6FWq+Uqh4iIriPbGYDBYMDQoUMBAOHh\n4Th48KBt3f79+zFgwACo1Wp4e3tDq9WivLxcrlKIiOgmZDsDMBqN0Gg0trZKpYLFYoGbmxuMRiO8\nvb1t67y8vGA0Gu32aTAY7qimlLjed/R+52tv9QLo5+oCbt+dfq4cof19NoF29/nkZ/MGsgWARqOB\nyWSyta1WK9zc3G66zmQyNQuEm4mMjJSnUCIiQcl2CSgiIgI7d+4EAJSVlSE0NNS2rl+/fjAYDGho\naMDFixdRVVXVbD0REclPIUmSJEfHVqsV6enpqKyshCRJyMrKws6dO6HVahEVFYW8vDzo9XpIkoSp\nU6fi6aeflqMMIiK6BdkCgIiI2jYOBCMiEhQDgIhIUAwAIiJBMQAIBQUF+Oijj1xdBnUgTU1NmDRp\nEhISElBXV+ewfh999FGH9UUyjgMgInHV1tbi3LlzKCgocHUp1AIGQAdTUFCAkpISXL58GbW1tRg/\nfjy2b9+Ow4cPY9asWTh9+jS+/fZbWCwWeHt7Y9GiRc3en5OTg82bN0OhUGDkyJEYP368i/aE2rM5\nc+bgP//5D959912YTCacO3cOADB79mw88MADiI6OxoABA1BTU4OHH34YFy9exP79+xEYGIiFCxei\nsrIS2dnZsFqtuHDhAmbPno2IiAhb/xUVFcjMzAQA+Pr6Iisry+5gUroJiTqUDRs2SBMnTpQkSZI2\nb94sjR07VrJardKePXukqVOnSosWLZKampokSZKk1157Tdq7d6+0YcMGaeHChdLhw4el+Ph4yWKx\nSE1NTVJiYqJUVVXlyt2hdurYsWNSbGystGDBAmnNmjWSJElSdXW1FB8fL0mSJIWFhUknTpyQzGaz\nFB4eLh0+fFiyWq3S8OHDpbq6OmnLli1SeXm5JEmSVFhYKL3//vuSJEnSI488IkmSJMXGxkqHDx+W\nJEmS8vLypE8++cTZu9gh8AygAwoLCwMAeHt7Izg4GAqFAj4+PmhsbIS7uzveeustdO7cGadPn4bF\nYrG9r7KyEidPnsSECRMAAHV1dTh69CiCgoJcsRvUAVRWVuKf//wntm3bBgC4cOECgCvf2gMCAgAA\nnTt3RkhICIArn9mGhgZ0794dixcvhoeHB0wmU7N5xQCgqqoKH3zwAQCgsbERgYGBztqlDoUB0AEp\nFIqbLm9sbERxcTHy8/NRX1+PmJgYSNeMAwwKCkJISAiWLVsGhUKBlStXcooOuiNBQUEYPXo0nnvu\nOfz222/Iz88HcOvP6FXz5s3DRx99hODgYOh0Opw4caLZ+sDAQMyfPx8BAQEwGAyora2VbR86MgaA\nQNzc3ODp6YmYmBio1Wr4+/vjzJkztvW9e/fGkCFDkJCQALPZjH79+uHuu+92YcXU3k2bNg3vv/8+\n8vLyYDQaMX369Fa9b/To0XjjjTfQrVs39OjRw/YbwlXp6elITU1FU1MTgCuBQbePU0EQEQmK4wCI\niATFACAiEhQDgIhIUAwAIiJBMQCIiATFACAiEhQDgIhIUAwAIjtOnz6NV155BTExMRg7dizKysqw\nf/9+JCQk4IUXXsBrr72GY8eOwWg04sknn8SePXsAAJMmTcKaNWtcXD3RrXEgGJEdn3/+OTw8PDB5\n8mTs3LkTFRUVKCoqwpdffomAgADs2rULy5cvx8qVK7Fnzx6kp6dj/PjxKCkpwbJly1xdPtEtMQCI\n7DAYDEhKSsKQIUMwbNgw9O7dG3FxcdBqtba/MRqN2L59OwBg7ty52Lx5M7Zt24bu3bu7qmwiuzgX\nEJEdkZGR2LJlC77//nts3boV+fn5uPfee7Fp0yYAV55+9d///hcAIEkSqqur4enpierqagYAtWn8\nDYDIjgULFqCwsBAvvPAC0tLSUF5ejrq6OuzduxcAsGHDBrz99tsAgLVr16Jz585YvHgx5syZA5PJ\n5MrSiVrES0BEdpw6dQopKSkwmUxQqVRITk6Gj48P5s2bh4aGBmg0GsyfPx8KhQIJCQnIz89Hz549\nkZGRAavVivT0dFfvAtFNMQCIiATFS0BERIJiABARCYoBQEQkKAYAEZGgGABERIJiABARCYoBQEQk\nqP8BpdGL+ejOrVAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb6ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 点图描述数据上的差异【趋势】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A8vPz8fz5c/j6+oLNZuPu3bvw9PTEvn37XvdWQhqV1OxirNl1G8/SCmVtpkZ8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\nogJACCE66v8B7ZCTx8+z0MEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xecc64e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"sex\", y=\"survived\", hue=\"class\", data=titanic);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#可以调整颜色和画图表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bVCoVn332mVULE0KIhmQ2K1DPL/ZGk2OsPVxnAKxYscIWdQghhFVptWpa+riR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xedbac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"class\", y=\"survived\", hue=\"sex\", data=titanic,\n",
    "              palette={\"male\": \"g\", \"female\": \"m\"},\n",
    "              markers=[\"^\", \"o\"], linestyles=[\"-\", \"--\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 宽型数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ixqh0RETEGJWOiIgYo9IRERFjVDp3gOTkZPbs2WN1DBG5SnJyMsnJyVbHME6lcwfIzMzk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\nRMQYlY6IiBij0hEREWP+HzxbcSr4WgToAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeecb860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=iris,orient=\"h\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 分类属性画图方法 【factorplot】####\n",
    "* 其实利用factorplot整合所有画图，这样操作可以调节参数我们观察区间的数据变化情况\n",
    "* 通过调节kind参数进行图形绘画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xefa4d68>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5LIZhkFdV+PgudApSSh6gQSJudXsOh4N+Vq7w6uWOoXaD0NeyN02bR7QSa0ia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\nQghhQSFJCCEsKCQJIYTF/wNUvglv7uPJnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xef99a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=tips)#默认的是pointplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xedfd518>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xede4048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=tips, kind=\"bar\") #误差范围默认服务范围在0.2左右"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xf0c7d68>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y/oT4u5JfUsNXW05RXF7HpBF+XDOy5yxoy6rINSVsAEW1pXyfupm7R9xqwah+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\nsSImJoaYmBjTcWRk5GVH0T7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EczRpjSsl/b0d6e/jxM6kSy5UNnGp/KpClp3YiE5vTPBs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\nQeD/A/H65dv0cO7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf079748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分组+条件关系进行调整【col：这个非常关键】\n",
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
    "               col=\"time\", data=tips, kind=\"swarm\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xf0463c8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9lJaWVqMfHrO9YtXpEcvOzlZIg9CAfkNyIztzAvcgJ9Xj9uWJKkJOEAxsKdoi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf0b6470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"time\", y=\"total_bill\", hue=\"smoker\",\n",
    "               col=\"day\", data=tips, kind=\"box\", size=4, aspect=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以下factorplot的配置属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xef88748>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x101f9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=tips, kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1041d588>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y/oT4u5JfUsNXW05RXF7HpBF+XDOy5yxoy6rINSVsAEW1pXyfupm7R9xqwah+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\nsSImJoaYmBjTcWRk5GVH0T7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EczRpjSsl/b0d6e/jxM6kSy5UNnGp/KpClp3YiE5vTPBs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\nQeD/A/H65dv0cO7WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10266748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"day\", y=\"total_bill\", hue=\"smoker\",\n",
    "               col=\"time\", data=tips, kind=\"swarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x104e46a0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9lJaWVqMfHrO9YtXpEcvOzlZIg9CAfkNyIztzAvcgJ9Xj9uWJKkJOEAxsKdoi\nIyMlXe6mf/rppzV9+nQtW7bMNztwZGSkcnNzTR0rIyOj3NenTp3ShQsX5QlrUOn9jB8Gxp45563y\nHEZZmUJsGEhbWFh4Xfvdqnv37tW+j505caPCwkJ/N8G0irJPTuxHTsiJGcGaE6ezbSDCt99+qylT\npig+Pl7Dhg3TihUrfN/Ly8vzLT5flWtflPDwcHnCGlj+V6QdwsPDgzJU1WFXTtwoPDxculTs72aY\nYnX2yYl55IScmBHMOXEyW67IzM7OVlJSkp5//nnf+qQ/+9nPlJ6eLknatWuXevToYcep4SDkBGaQ\nE5hBThAMbCna/vVf/1UXL17Ua6+9psTERCUmJmr69OlKTU3VqFGjVFxcXO7aAwQncgIzyAnMICcI\nBrZ8PDp37txya5Ve8c4779hxOlRg7dq12rp1a7ltZWVl1TrGtcPjH374YSUlJdW6bRI5gTnkBGaQ\nEwQDJtcFAAAWMFSWX2Lp7All+SXyquoBhcGCos3FkpKSLOsVAwAA/kXRBgAALOBRSIN6ls95GhUV\nZdnxnI6iDQBQBT72AgIBRRsQ5Mz+Mi4rKpWkKmfGL8svkfjDGAAsR9EGBDOPRyEej5pHNaty1ytL\nw1W5b5QUHR1tResQMPjYy42M4nx5D2+rfJ/SIkmSp159M0e0oFWoDEUbEMQ8oRFq3jRKa9asqXLf\nK+v4mtkXQGAz+4eV74+1plUX2NnZBbVqE6pG0QYAQJBZvny5qf2q88fahAkTlO09W6t2oXKOK9q8\nXq+pLt3qMXzX6wAAAAQixxVtQE2tXbtWaWlp5bZ5vZdHr117bU2fPn0Cbo47O/5gMYrz5WUAHwA4\nguOKtqioKOUXS1EdHrLsmLkHN1Y5Ig7uVFBw+RoMMxdEW7EsmGTv0mAAAPdyXNEG1NSNVohw0sX1\ndvzB4j28jRF8AOAQFG2ASSwLBgDwJ4o2Scz2DQAAAl1I1bsAAADA3+hpk8Rs3wBQOZY7gxnkxF4U\nbQCAyrHcGUyozutJTmqGog0AUCmWO4MZZldZkMhJTXFNGwAAgANQtAEAADgARRsAAIADOPKaNjPr\nLxqlRZIkT736Zo5oQasQSGbOnKmcnJwq97tyMeyV6yuqEh0dXa3rNgB/MLtGrdn3SaM4XwzhA/zP\ncUWb2VEkvpEpTat+o8nOLqhVmxB4cnJydPr0GXnCGlS6n/FDZ/OZc1VPhHz5FxcQ2Go0gq/K98ko\nRvABAcBxRZvZXo7qjEyZMGGCsr1na9UuBB5PWAPL1+kEAh0j+AD3clzRBpjh9XpNf0RkllGcLy8r\nkwEA/ISiDcB11q5dq7S0tHJDCaGAAAAJJUlEQVTbKrr+r0+fPkpKSqqztgFAsKJogytFRUUpv1iW\nfzwazEuTRURE+LsJABDUKNoAB6mrUYFJSUn0ngFAgKFo+wGL3CLQMSoQAIIbRZtY5BbOwKhAAAhu\nFG3ilyEAAAh8LGMFAADgABRtAAAADmDrx6NffvmlVq5cqXXr1un48eNKSUmRx+NRx44dtWDBAoWE\nUDPCvpxYvUYt6y/6F+8nMIOcwM1sK9reeOMNbdu2TQ0aXF77ccmSJZo+fbri4uI0f/587dixQ4MG\nDbLkXNWZCFRiMtBAYldO7FijlpGW/lOX7ydwLnICt7OtaLvllluUmpqqmTNnSpKysrLUs2dPSVK/\nfv2UlpZm6w8PE4E6g105sWONWviPv99P4AzkBG5nW9E2ZMgQnTx50ve1YRjyeDySpMjISOXm5po6\nTkZGRpX7dOvWTd26datW+8wc90YKCwtrdX+36t69e43uV5c5uRG3vp6B+ricmhO3siMnVhyTnAQO\nu95L/JkTJ6uzKT+uvo4gLy9PjRs3NnW/QHtRwsPDJQVeu9yirnPi1tfTrY/rCre8n/ibHTkJpOyR\nk9qz6/UMpJw4SZ1dkfmzn/1M6enpkqRdu3apR48edXVqOAg5gRnkBGaQE7hNnRVtycnJSk1N1ahR\no1RcXKwhQ4bU1anhIOQEZpATmEFO4Da2fjzatm1bvffee5KkmJgYvfPOO3aeDg5FTmAGOYEZ5CTw\nVGeGB2Z3qBzLWCFo8MYBAIGBGR5qhqINQY03DgCwV1JSEn8EW4SiDUGDNw4AgJOxngcAAIAD0NNW\ngRtd/yRxDRQAAPAPirZq4hooAADgDxRtFeD6JwCoHCOy3aU6r6fEa+oPFG0AAMvwaYS78HoGFoo2\nAECN8ImEu/B6Bj5GjwIAADgARRsAAIADULQBAAA4ANe0AQ7GfIIAEDwo2gAXYsQXALgPRRvgYIz2\nAoDgwTVtAAAADkDRBgAA4AAUbQAAAA5A0QYAAOAAFG0AAAAOQNEGAADgABRtAAAADkDRBgAA4ABM\nrgsALsdyZ4A7ULQBQJBiuTPAWSjaAMDlWO4McAeuaQMAAHAAijYAAAAHoGgDAABwAIo2AAAAB6Bo\nAwAAcACKNgAAAAegaAMAAHAAijYAAAAHCPjJdTMyMvzdBJjUvXt3v52bnDgHOYEZ5ARm+DMn/uAx\nDMPwdyMAAABQOT4eBQAAcACKNgAAAAegaAMAAHAAijYAAAAHoGgDAABwAFcWbenp6ZoxY4alx0xJ\nSdGuXbssPaZZ6enp6t27txITE5WQkKDRo0frT3/6kw4ePKhXXnnFL21yA3ICM8gJzCAnqAsBP08b\nLuvVq5dWrVolScrLy1NiYqIWLVqkqVOn+rllCCTkBGaQE5hBTgJP0BRtAwYM0Pbt2xUeHq6VK1fq\n9ttvV5s2bfTGG28oLCxMJ0+e1P3336/Jkyfr2LFjmjt3roqLixUREeEL7caNG/Xmm2/K6/Vq4cKF\nuuOOO/zyWCIjIzVq1Ci98MIL+slPfqJVq1Zp8ODBuuuuu3T06FFFR0crNTVVW7du1X/913+poKBA\nf//73zVx4kQNHz5cX3/9tX7zm99Ikpo0aaLFixfrq6++0sqVKxUWFqbHHntMjzzyiF8em7+RE3Ji\nBjkhJ2aQE3JitaAp2ipy6tQpbdu2TUVFRbr33ns1efJkLVu2TJMmTVK/fv30pz/9SV999ZUkqUuX\nLnrqqae0ZcsWbdmyxW8/PJIUHR2tc+fO6Sc/+Ykk6cSJE3r77bfVqlUrjR49WpmZmZIkr9erNWvW\n6NixY3ryySc1fPhwzZs3T4sXL1aHDh20adMmvfnmm7rnnntUWFioTZs2+e0xBTJyQk7MICfkxAxy\nQk5qKiiLtqsXgejUqZNCQ0MVGhqqiIgISdLRo0d15513SpLuv/9+SdK///u/q0uXLpKk5s2bq6Cg\noI5bXd6pU6f00EMP6W9/+5skqWnTpmrVqpUkqVWrViosLJQkde7c2betqKhIknTkyBH96le/kiQV\nFxcrJiZGknz/4zJyQk7MICfkxAxyQk6sEDRFW/369XX69Gm1bdtWhw4dUvv27SVJHo/nun3bt2+v\nzMxM3XPPPdq2bZsuXLhQ4b7+4PV6tWnTJo0dO9a3raK23Wh7TEyMli1bptatWysjI0NnzpyRJIWE\nuHJcSrWQkx+Rk4qRkx+Rk4qRkx+RE2u4tmhLS0vT8OHDfV+PHz9ekyZNUps2bdS4ceNK7ztz5kzN\nnz9fq1evVkREhFasWKGsrCy7m1ypPXv2KDExUSEhISotLdW0adN00003KT09vdrHWrhwoZKTk1Va\nWipJWrRokU6fPm11kx2BnFSMnPyInFSMnPyInFSMnFiDBeMBAAAcgH5JAAAAB6BoAwAAcACKNgAA\nAAegaAMAAHAAijYAAAAHoGizSG5urqZMmaLvv/9eEydO9HdzEKDICcwgJzCDnAQfijaLXLhwQQcP\nHlTLli31xhtv+Ls5CFDkBGaQE5hBToIP87RZ5Mknn9Rf/vIX9e/fXwcPHtSnn36qlJQUNWjQQF99\n9ZUuXryof/mXf9HWrVt16NAhDRw4UCkpKSotLdXy5cu1d+9elZaWavjw4Ro3bpy/Hw5sQk5gBjmB\nGeQkCBmwxIkTJ4z77rvP979hGEZycrLx1FNPGYZhGFu2bDG6d+9uZGdnG7m5ucadd95pXLx40Vi/\nfr2xePFiwzAMo7Cw0EhISDD27dvnt8cBe5ETmEFOYAY5CT6uXcYqUPTr10+S1Lp1a3Xs2FHR0dGS\npCZNmujChQvavXu3Dh48qD179kiSLl26pK+//lo9evTwW5tR98gJzCAnMIOcuBdFm83CwsJ8t0ND\nr3+6S0tL9fzzz2vw4MGSpLNnzyoyMrLO2ofAQE5gBjmBGeTEvRiIYJHQ0FCVlJRU+369evXSe++9\np+LiYuXl5Sk+Pl779++3oYUIBOQEZpATmEFOgg89bRaJjo5W69atNWvWrGrdb/To0Tp+/Lh++ctf\nqqSkRMOHD1dcXJxNrYS/kROYQU5gBjkJPoweBQAAcAA+HgUAAHAAijYAAAAHoGgDAABwAIo2AAAA\nB6BoAwAAcACKNgAAAAegaAMAAHAAijYAAAAH+H9CqieFDuodhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104152e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"time\", y=\"total_bill\", hue=\"smoker\",\n",
    "               col=\"day\", data=tips, kind=\"box\", size=4, aspect=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "seaborn.factorplot(x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None, kind='point', size=4, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs)\n",
    "\n",
    "### Parameters： ###\n",
    "\n",
    "* x,y,hue 数据集变量 变量名\n",
    "* date 数据集 数据集名\n",
    "* row,col 更多分类变量进行平铺显示 变量名\n",
    "* col_wrap 每行的最高平铺数 整数\n",
    "* estimator 在每个分类中进行矢量到标量的映射 矢量\n",
    "* ci 置信区间 浮点数或None\n",
    "* n_boot 计算置信区间时使用的引导迭代次数 整数\n",
    "* units 采样单元的标识符，用于执行多级引导和重复测量设计 数据变量或向量数据\n",
    "* order, hue_order 对应排序列表 字符串列表\n",
    "* row_order, col_order 对应排序列表 字符串列表\n",
    "* kind : 可选：point 默认, bar 柱形图, count 频次, box 箱体, violin 提琴, strip 散点，swarm 分散点\n",
    "size 每个面的高度（英寸） 标量\n",
    "aspect 纵横比 标量\n",
    "orient 方向 \"v\"/\"h\"\n",
    "color 颜色 matplotlib颜色\n",
    "palette 调色板 seaborn颜色色板或字典\n",
    "legend hue的信息面板 True/False\n",
    "legend_out 是否扩展图形，并将信息框绘制在中心右边 True/False\n",
    "share{x,y} 共享轴线 True/False"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
